Abstract
Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.
Highlights
Monitoring wild animals to identify their habitats and populations is considered to be important for the conservation and management of ecosystems, as well as because human health can be significantly affected by these ecosystems
It was occasionally observed that the ground truth boxes were inaccurately observed that the Single Shot MultiBox Detector (SSD) models and You Only Look Once (YOLO) models show reverse performance for different labeled as truth that were very compared to the entire aerial photograph, and this situation thresholds
Region-based Convolutional Neural Network (R-convolutional neural networks (CNN)), Region-based Fully Convolutional Network (R-FCN), SSD, Retinanet, and YOLO, to create bird detection models using aerial photographs captured by unmanned aerial vehicle (UAV)
Summary
Monitoring wild animals to identify their habitats and populations is considered to be important for the conservation and management of ecosystems, as well as because human health can be significantly affected by these ecosystems. Wildlife populations have been surveyed using several counting methods, such as the total ground count method, the line-transect count method, the dropping count method, and aerial count method. These methods are based on the use of human observation to directly count the birds in local areas, and this information is used to estimate the size of the population in an entire area [2]. The line-transect count method, which estimates the total population by measuring the number and distance of targets [4,5], shows
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